Managing throughput fairness and quality of service in file systems

ABSTRACT

Embodiments are directed to managing file systems over a network. Jobs may be provided to a storage computer in a file system. Control models may be associated with the jobs. Scores may be generated based on the control models. Each job may be associated with a score provided by its associated control model. And, each job that may be behind its corresponding schedule may be associated with a higher score value than each other job that may be either on its corresponding other schedule or ahead of its corresponding other schedule. Commands may be selected for execution based on the commands being associated with a job that may be associated with the higher score value that may be greater than score values associated with other jobs. The jobs may be ranked based on the updated scores. Subsequent commands may be selected and executed based on the ranking of the jobs.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Utility Patent Application is a Continuation of U.S. patentapplication Ser. No. 16/752,509 filed on Jan. 24, 2020, now U.S. Pat.No. 10,860,372 issued on Dec. 8, 2020, the benefit of the filing date ofwhich is hereby claimed under 35 U.S.C. § 120 and the contents of whichis further incorporated in entirety by reference.

TECHNICAL FIELD

The present invention relates generally to file systems, and moreparticularly, but not exclusively, to managing service allocation fordistributed file systems.

BACKGROUND

Modern computing often requires the collection, processing, or storageof very large data sets or file systems. Accordingly, to accommodate thecapacity requirements as well as other requirements, such as, highavailability, redundancy, latency/access considerations, or the like,modern file systems may be very large or distributed across multiplehosts, networks, or data centers, and so on. In many cases, distributedfile systems may be comprised of many storage devices (e.g., harddrives, solid state drives, or the like) that may independentlyexperience failures. Accordingly, many file systems may execute avariety of maintenance or support jobs to maintain stability,correctness, availability, or the like. In some cases, such jobs may belong running or resource intensive. Also, in some cases, some user jobsmay be long running or resource intensive. But, often, many user jobsmay be short running jobs where users expect low latency and a highlevel of responsiveness. However, resources consumed by some longrunning jobs may reduce the responsiveness of some user jobs that usersmay otherwise expect to be responsive. Thus, it is with respect to theseconsiderations and others that the present invention has been made.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the present innovationsare described with reference to the following drawings. In the drawings,like reference numerals refer to like parts throughout the variousfigures unless otherwise specified. For a better understanding of thedescribed innovations, reference will be made to the following DetailedDescription of Various Embodiments, which is to be read in associationwith the accompanying drawings, wherein:

FIG. 1 illustrates a system environment in which various embodiments maybe implemented;

FIG. 2 illustrates a schematic embodiment of a client computer;

FIG. 3 illustrates a schematic embodiment of a network computer;

FIG. 4 illustrates a logical architecture of a file system for managingthroughput fairness and quality of service in file systems in accordancewith one or more of the various embodiments;

FIG. 5 illustrates a logical schematic of a system for managingthroughput fairness and quality of service in file systems in accordancewith one or more of the various embodiments;

FIG. 6 illustrates a logical schematic of a client job for managingthroughput fairness and quality of service in file systems in accordancewith one or more of the various embodiments;

FIG. 7 illustrates a logical schematic of a control model for managingthroughput fairness and quality of service in file systems in accordancewith one or more of the various embodiments;

FIG. 8 illustrates an overview flowchart for a process for managingthroughput fairness and quality of service in file systems in accordancewith one or more of the various embodiments;

FIG. 9 illustrates a flowchart for a process for managing throughputfairness and quality of service in file systems in accordance with oneor more of the various embodiments;

FIG. 10 illustrates a flowchart for a process for using a control modelfor managing throughput fairness and quality of service in file systemsin accordance with one or more of the various embodiments;

FIG. 11 illustrates a flowchart for a process for managing throughputfairness and quality of service in file systems in accordance with oneor more of the various embodiments; and

FIG. 12 illustrates a flowchart for a process for evaluating controlmodels for managing throughput fairness and quality of service in filesystems in accordance with one or more of the various embodiments.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

Various embodiments now will be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific exemplary embodiments bywhich the invention may be practiced. The embodiments may, however, beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will be thorough and complete, and willfully convey the scope of the embodiments to those skilled in the art.Among other things, the various embodiments may be methods, systems,media or devices. Accordingly, the various embodiments may take the formof an entirely hardware embodiment, an entirely software embodiment oran embodiment combining software and hardware aspects. The followingdetailed description is, therefore, not to be taken in a limiting sense.

Throughout the specification and claims, the following terms take themeanings explicitly associated herein, unless the context clearlydictates otherwise. The phrase “in one embodiment” as used herein doesnot necessarily refer to the same embodiment, though it may.Furthermore, the phrase “in another embodiment” as used herein does notnecessarily refer to a different embodiment, although it may. Thus, asdescribed below, various embodiments may be readily combined, withoutdeparting from the scope or spirit of the invention.

In addition, as used herein, the term “or” is an inclusive “or”operator, and is equivalent to the term “and/or,” unless the contextclearly dictates otherwise. The term “based on” is not exclusive andallows for being based on additional factors not described, unless thecontext clearly dictates otherwise. In addition, throughout thespecification, the meaning of “a,” “an,” and “the” include pluralreferences. The meaning of “in” includes “in” and “on.”

For example embodiments, the following terms are also used hereinaccording to the corresponding meaning, unless the context clearlydictates otherwise.

As used herein the term, “engine” refers to logic embodied in hardwareor software instructions, which can be written in a programminglanguage, such as C, C++, Objective-C, COBOL, Java™, PHP, Perl,JavaScript, Ruby, VBScript, Microsoft .NET™ languages such as C#, or thelike. An engine may be compiled into executable programs or written ininterpreted programming languages. Software engines may be callable fromother engines or from themselves. Engines described herein refer to oneor more logical modules that can be merged with other engines orapplications, or can be divided into sub-engines. The engines can bestored in non-transitory computer-readable medium or computer storagedevices and be stored on and executed by one or more general purposecomputers, thus creating a special purpose computer configured toprovide the engine.

As used herein the terms “file system object,” or “object” refer toentities stored in a file system. These may include files, directories,or the like. In this document for brevity and clarity all objects storedin a file system may be referred to as file system objects.

As used herein the term “file system” refers to storage systems that mayinclude one or more storage devices, one or more servers, one or morenodes, or the like. Typically, file systems may be arranged to supportone or more conventional/standards-based file system protocols, or thelike. In some cases, file systems may be distributed across multiplenodes, servers, networks, or the like.

As used herein the term “storage unit” refers to storage component in afile system. Accordingly, storage devices, storage enclosures, clusternodes, clusters, or the like, be considered storage units. Theparticular component represented by a storage unit may depend oncontext. For example, in some cases, a single hard drive may beconsidered a storage unit, where in other cases, a node computer indistributed file system may be considered a single storage unit, eventhough the node computer may include server hard drives, solid statedrives, or the like.

As used herein the term “configuration information” refers toinformation that may include rule based policies, pattern matching,scripts (e.g., computer readable instructions), parameter values,settings, or the like, that may be provided from various sources,including, configuration files, databases, user input, built-indefaults, or the like, or combination thereof.

The following briefly describes embodiments of the invention in order toprovide a basic understanding of some aspects of the invention. Thisbrief description is not intended as an extensive overview. It is notintended to identify key or critical elements, or to delineate orotherwise narrow the scope. Its purpose is merely to present someconcepts in a simplified form as a prelude to the more detaileddescription that is presented later.

Briefly stated, various embodiments are directed to managing filesystems over a network. In one or more of the various embodiments, oneor more jobs (client jobs) may be provided to a storage computer in afile system. In some embodiments, the storage computer may be associatedwith one or more storage devices. And, in some embodiments, the filesystem may include one or more storage computers.

In one or more of the various embodiments, one or more control modelsmay be associated with the one or more jobs based on one or morecharacteristics of the one or more jobs. In one or more of the variousembodiments, associating the one or more control models with the one ormore jobs may include: determining one or more interactive jobs and oneor more long-running jobs based on the one or more characteristic;associating each interactive job a first type of control model such thatscore values generated by the first control model may be equivalent tozero; associating each long-running jobs with a second type of controlmodel such that score values generated by the second type of controlmodel may be based on a remainder of work to be completed by the eachlong-running job; or the like.

In one or more of the various embodiments, one or more scores (targetscores) may be generated based on the one or more control modelsassociated with each job. In some embodiments, each job may beassociated with a score provided by its associated control model. And,in some embodiments, each job that may be behind its correspondingschedule may be associated with a higher score value than each other jobthat may be either on its corresponding other schedule or ahead of itscorresponding other schedule. In one or more of the various embodiments,generating the one or more scores may include: providing a portion ofthe one or more metrics to a control model associated with the job;generating an error value based on a difference of a setpoint value andthe portion of the one or more metrics such that the setpoint value maybe defined by the control model; providing the error value to one ormore functions, including a current value function, a historic function,or a rate of change function to generate a score such that the score maybe a dimensionless scalar value; or the like.

In one or more of the various embodiments, one or more commands may beselected for execution on the one or more storage devices based on theone or more commands being associated with a job that may be associatedwith the higher score value that may be greater than one or more scorevalues associated with one or more other jobs. In one or more of thevarious embodiments, selecting the one or more commands for executionmay include: increasing a number of commands for the one or more jobsthat may be behind schedule; decreasing the number of commands for theone or more other jobs if the one or more jobs that may be ahead ofschedule; or the like.

In one or more of the various embodiments, in response to one job beingprovided to the storage computer, selecting the one or more commands forexecution may include: executing one command if the one job is onschedule; executing one command if the one job is ahead of schedule;executing more than one command when the one job is behind of schedule;or the like.

In one or more of the various embodiments, the one or more jobs may beranked based on the one or more updated scores. In some embodiments, oneor more subsequent commands may be selected and executed based on theranking of the one or more jobs.

In one or more of the various embodiments, the one or more metricsassociated with the execution of the one or more jobs may be monitoredsuch that the one or more metrics may be defined by each control model.

In one or more of the various embodiments, the one or more scores may beupdated based on the one or more control models and one or more metrics.In one or more of the various embodiments, updating the one or morescores may include: employing the one or more control models and the oneor more metrics to determine one or more executing jobs that may be oneor more of ahead-of-schedule, on-schedule, or behind-schedule;increasing each score associated with each behind-schedule executingjob; decreasing each score associated with each ahead-of-scheduleexecuting job; setting each score associated with each on-schedule jobto zero; or the like.

Illustrated Operating Environment

FIG. 1 shows components of one embodiment of an environment in whichembodiments of the invention may be practiced. Not all of the componentsmay be required to practice the invention, and variations in thearrangement and type of the components may be made without departingfrom the spirit or scope of the invention. As shown, system 100 of FIG.1 includes local area networks (LANs)/wide area networks(WANs)—(network) 110, wireless network 108, client computers 102-105,file system management server computer 116, or the like.

At least one embodiment of client computers 102-105 is described in moredetail below in conjunction with FIG. 2. In one embodiment, at leastsome of client computers 102-105 may operate over one or more wired orwireless networks, such as networks 108, or 110. Generally, clientcomputers 102-105 may include virtually any computer capable ofcommunicating over a network to send and receive information, performvarious online activities, offline actions, or the like. In oneembodiment, one or more of client computers 102-105 may be configured tooperate within a business or other entity to perform a variety ofservices for the business or other entity. For example, client computers102-105 may be configured to operate as a web server, firewall, clientapplication, media player, mobile telephone, game console, desktopcomputer, or the like. However, client computers 102-105 are notconstrained to these services and may also be employed, for example, asfor end-user computing in other embodiments. It should be recognizedthat more or less client computers (as shown in FIG. 1) may be includedwithin a system such as described herein, and embodiments are thereforenot constrained by the number or type of client computers employed.

Computers that may operate as client computer 102 may include computersthat typically connect using a wired or wireless communications mediumsuch as personal computers, multiprocessor systems, microprocessor-basedor programmable electronic devices, network PCs, or the like. In someembodiments, client computers 102-105 may include virtually any portablecomputer capable of connecting to another computer and receivinginformation such as, laptop computer 103, mobile computer 104, tabletcomputers 105, or the like. However, portable computers are not solimited and may also include other portable computers such as cellulartelephones, display pagers, radio frequency (RF) devices, infrared (IR)devices, Personal Digital Assistants (PDAs), handheld computers,wearable computers, integrated devices combining one or more of thepreceding computers, or the like. As such, client computers 102-105typically range widely in terms of capabilities and features. Moreover,client computers 102-105 may access various computing applications,including a browser, or other web-based application.

A web-enabled client computer may include a browser application that isconfigured to send requests and receive responses over the web. Thebrowser application may be configured to receive and display graphics,text, multimedia, and the like, employing virtually any web-basedlanguage. In one embodiment, the browser application is enabled toemploy JavaScript, HyperText Markup Language (HTML), eXtensible MarkupLanguage (XML), JavaScript Object Notation (JSON), Cascading StyleSheets (CS S), or the like, or combination thereof, to display and senda message. In one embodiment, a user of the client computer may employthe browser application to perform various activities over a network(online). However, another application may also be used to performvarious online activities.

Client computers 102-105 also may include at least one other clientapplication that is configured to receive or send content betweenanother computer. The client application may include a capability tosend or receive content, or the like. The client application may furtherprovide information that identifies itself, including a type,capability, name, and the like. In one embodiment, client computers102-105 may uniquely identify themselves through any of a variety ofmechanisms, including an Internet Protocol (IP) address, a phone number,Mobile Identification Number (MIN), an electronic serial number (ESN), aclient certificate, or other device identifier. Such information may beprovided in one or more network packets, or the like, sent between otherclient computers, file system management server computer 116, or othercomputers. Client computers 102-105 may further be configured to includea client application that enables an end-user to log into an end-useraccount that may be managed by another computer, such as file systemmanagement server computer 116, or the like. Such an end-user account,in one non-limiting example, may be configured to enable the end-user tomanage one or more online activities, including in one non-limitingexample, project management, software development, systemadministration, configuration management, search activities, socialnetworking activities, browse various websites, communicate with otherusers, or the like. Also, client computers may be arranged to enableusers to display reports, interactive user-interfaces, or resultsprovided by file system management server computer 116.

Wireless network 108 is configured to couple client computers 103-105and its components with network 110. Wireless network 108 may includeany of a variety of wireless sub-networks that may further overlaystand-alone ad-hoc networks, and the like, to provide aninfrastructure-oriented connection for client computers 103-105. Suchsub-networks may include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. In one embodiment, the system mayinclude more than one wireless network.

Wireless network 108 may further include an autonomous system ofterminals, gateways, routers, and the like connected by wireless radiolinks, and the like. These connectors may be configured to move freelyand randomly and organize themselves arbitrarily, such that the topologyof wireless network 108 may change rapidly.

Wireless network 108 may further employ a plurality of accesstechnologies including 2nd (2G), 3rd (3G), 4th (4G) 5th (5G) generationradio access for cellular systems, WLAN, Wireless Router (WR) mesh, andthe like. Access technologies such as 2G, 3G, 4G, 5G, and future accessnetworks may enable wide area coverage for mobile computers, such asclient computers 103-105 with various degrees of mobility. In onenon-limiting example, wireless network 108 may enable a radio connectionthrough a radio network access such as Global System for Mobilcommunication (GSM), General Packet Radio Services (GPRS), Enhanced DataGSM Environment (EDGE), code division multiple access (CDMA), timedivision multiple access (TDMA), Wideband Code Division Multiple Access(WCDMA), High Speed Downlink Packet Access (HSDPA), Long Term Evolution(LTE), and the like. In essence, wireless network 108 may includevirtually any wireless communication mechanism by which information maytravel between client computers 103-105 and another computer, network, acloud-based network, a cloud instance, or the like.

Network 110 is configured to couple network computers with othercomputers, including, file system management server computer 116, clientcomputers 102, and client computers 103-105 through wireless network108, or the like. Network 110 is enabled to employ any form of computerreadable media for communicating information from one electronic deviceto another. Also, network 110 can include the Internet in addition tolocal area networks (LANs), wide area networks (WANs), directconnections, such as through a universal serial bus (USB) port, Ethernetport, other forms of computer-readable media, or any combinationthereof. On an interconnected set of LANs, including those based ondiffering architectures and protocols, a router acts as a link betweenLANs, enabling messages to be sent from one to another. In addition,communication links within LANs typically include twisted wire pair orcoaxial cable, while communication links between networks may utilizeanalog telephone lines, full or fractional dedicated digital linesincluding T1, T2, T3, and T4, or other carrier mechanisms including, forexample, E-carriers, Integrated Services Digital Networks (ISDNs),Digital Subscriber Lines (DSLs), wireless links including satellitelinks, or other communications links known to those skilled in the art.Moreover, communication links may further employ any of a variety ofdigital signaling technologies, including without limit, for example,DS-0, DS-1, DS-2, DS-3, DS-4, OC-3, OC-12, OC-48, or the like.Furthermore, remote computers and other related electronic devices couldbe remotely connected to either LANs or WANs via a modem and temporarytelephone link. In one embodiment, network 110 may be configured totransport information of an Internet Protocol (IP).

Additionally, communication media typically embodies computer readableinstructions, data structures, program modules, or other transportmechanisms and includes any information non-transitory delivery media ortransitory delivery media. By way of example, communication mediaincludes wired media such as twisted pair, coaxial cable, fiber optics,wave guides, and other wired media and wireless media such as acoustic,RF, infrared, and other wireless media.

Also, one embodiment of file system management server computer 116 isdescribed in more detail below in conjunction with FIG. 3. Although FIG.1 illustrates file system management server computer 116, or the like,each as a single computer, the innovations or embodiments are not solimited. For example, one or more functions of file system managementserver computer 116, or the like, may be distributed across one or moredistinct network computers. Moreover, in one or more embodiments, filesystem management server computer 116 may be implemented using aplurality of network computers. Further, in one or more of the variousembodiments, file system management server computer 116, or the like,may be implemented using one or more cloud instances in one or morecloud networks. Accordingly, these innovations and embodiments are notto be construed as being limited to a single environment, and otherconfigurations, and other architectures are also envisaged.

Illustrative Client Computer

FIG. 2 shows one embodiment of client computer 200 that may include manymore or less components than those shown. Client computer 200 mayrepresent, for example, one or more embodiment of mobile computers orclient computers shown in FIG. 1.

Client computer 200 may include processor 202 in communication withmemory 204 via bus 228. Client computer 200 may also include powersupply 230, network interface 232, audio interface 256, display 250,keypad 252, illuminator 254, video interface 242, input/output interface238, haptic interface 264, global positioning systems (GPS) receiver258, open air gesture interface 260, temperature interface 262,camera(s) 240, projector 246, pointing device interface 266,processor-readable stationary storage device 234, and processor-readableremovable storage device 236. Client computer 200 may optionallycommunicate with a base station (not shown), or directly with anothercomputer. And in one embodiment, although not shown, a gyroscope may beemployed within client computer 200 to measure or maintain anorientation of client computer 200.

Power supply 230 may provide power to client computer 200. Arechargeable or non-rechargeable battery may be used to provide power.The power may also be provided by an external power source, such as anAC adapter or a powered docking cradle that supplements or recharges thebattery.

Network interface 232 includes circuitry for coupling client computer200 to one or more networks, and is constructed for use with one or morecommunication protocols and technologies including, but not limited to,protocols and technologies that implement any portion of the OSI modelfor mobile communication (GSM), CDMA, time division multiple access(TDMA), UDP, TCP/IP, SMS, MMS, GPRS, WAP, UWB, WiMax, SIP/RTP, GPRS,EDGE, WCDMA, LTE, UMTS, OFDM, CDMA2000, EV-DO, HSDPA, or any of avariety of other wireless communication protocols. Network interface 232is sometimes known as a transceiver, transceiving device, or networkinterface card (MC).

Audio interface 256 may be arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 256 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others or generate an audio acknowledgment forsome action. A microphone in audio interface 256 can also be used forinput to or control of client computer 200, e.g., using voicerecognition, detecting touch based on sound, and the like.

Display 250 may be a liquid crystal display (LCD), gas plasma,electronic ink, light emitting diode (LED), Organic LED (OLED) or anyother type of light reflective or light transmissive display that can beused with a computer. Display 250 may also include a touch interface 244arranged to receive input from an object such as a stylus or a digitfrom a human hand, and may use resistive, capacitive, surface acousticwave (SAW), infrared, radar, or other technologies to sense touch orgestures.

Projector 246 may be a remote handheld projector or an integratedprojector that is capable of projecting an image on a remote wall or anyother reflective object such as a remote screen.

Video interface 242 may be arranged to capture video images, such as astill photo, a video segment, an infrared video, or the like. Forexample, video interface 242 may be coupled to a digital video camera, aweb-camera, or the like. Video interface 242 may comprise a lens, animage sensor, and other electronics. Image sensors may include acomplementary metal-oxide-semiconductor (CMOS) integrated circuit,charge-coupled device (CCD), or any other integrated circuit for sensinglight.

Keypad 252 may comprise any input device arranged to receive input froma user. For example, keypad 252 may include a push button numeric dial,or a keyboard. Keypad 252 may also include command buttons that areassociated with selecting and sending images.

Illuminator 254 may provide a status indication or provide light.Illuminator 254 may remain active for specific periods of time or inresponse to event messages. For example, when illuminator 254 is active,it may back-light the buttons on keypad 252 and stay on while the clientcomputer is powered. Also, illuminator 254 may back-light these buttonsin various patterns when particular actions are performed, such asdialing another client computer. Illuminator 254 may also cause lightsources positioned within a transparent or translucent case of theclient computer to illuminate in response to actions.

Further, client computer 200 may also comprise hardware security module(HSM) 268 for providing additional tamper resistant safeguards forgenerating, storing or using security/cryptographic information such as,keys, digital certificates, passwords, passphrases, two-factorauthentication information, or the like. In some embodiments, hardwaresecurity module may be employed to support one or more standard publickey infrastructures (PKI), and may be employed to generate, manage, orstore key pairs, or the like. In some embodiments, HSM 268 may be astand-alone computer, in other cases, HSM 268 may be arranged as ahardware card that may be added to a client computer.

Client computer 200 may also comprise input/output interface 238 forcommunicating with external peripheral devices or other computers suchas other client computers and network computers. The peripheral devicesmay include an audio headset, virtual reality headsets, display screenglasses, remote speaker system, remote speaker and microphone system,and the like. Input/output interface 238 can utilize one or moretechnologies, such as Universal Serial Bus (USB), Infrared, WiFi, WiMax,Bluetooth™, and the like.

Input/output interface 238 may also include one or more sensors fordetermining geolocation information (e.g., GPS), monitoring electricalpower conditions (e.g., voltage sensors, current sensors, frequencysensors, and so on), monitoring weather (e.g., thermostats, barometers,anemometers, humidity detectors, precipitation scales, or the like), orthe like. Sensors may be one or more hardware sensors that collect ormeasure data that is external to client computer 200.

Haptic interface 264 may be arranged to provide tactile feedback to auser of the client computer. For example, the haptic interface 264 maybe employed to vibrate client computer 200 in a particular way whenanother user of a computer is calling. Temperature interface 262 may beused to provide a temperature measurement input or a temperaturechanging output to a user of client computer 200. Open air gestureinterface 260 may sense physical gestures of a user of client computer200, for example, by using single or stereo video cameras, radar, agyroscopic sensor inside a computer held or worn by the user, or thelike. Camera 240 may be used to track physical eye movements of a userof client computer 200.

GPS transceiver 258 can determine the physical coordinates of clientcomputer 200 on the surface of the Earth, which typically outputs alocation as latitude and longitude values. GPS transceiver 258 can alsoemploy other geo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), Enhanced Observed Time Difference(E-OTD), Cell Identifier (CI), Service Area Identifier (SAI), EnhancedTiming Advance (ETA), Base Station Subsystem (BSS), or the like, tofurther determine the physical location of client computer 200 on thesurface of the Earth. It is understood that under different conditions,GPS transceiver 258 can determine a physical location for clientcomputer 200. In one or more embodiments, however, client computer 200may, through other components, provide other information that may beemployed to determine a physical location of the client computer,including for example, a Media Access Control (MAC) address, IP address,and the like.

In at least one of the various embodiments, applications, such as,operating system 206, other client apps 224, web browser 226, or thelike, may be arranged to employ geo-location information to select oneor more localization features, such as, time zones, languages,currencies, calendar formatting, or the like. Localization features maybe used in display objects, data models, data objects, user-interfaces,reports, as well as internal processes or databases. In at least one ofthe various embodiments, geo-location information used for selectinglocalization information may be provided by GPS 258. Also, in someembodiments, geolocation information may include information providedusing one or more geolocation protocols over the networks, such as,wireless network 108 or network 111.

Human interface components can be peripheral devices that are physicallyseparate from client computer 200, allowing for remote input or outputto client computer 200. For example, information routed as describedhere through human interface components such as display 250 or keyboard252 can instead be routed through network interface 232 to appropriatehuman interface components located remotely. Examples of human interfaceperipheral components that may be remote include, but are not limitedto, audio devices, pointing devices, keypads, displays, cameras,projectors, and the like. These peripheral components may communicateover a Pico Network such as Bluetooth™, Zigbee™ and the like. Onenon-limiting example of a client computer with such peripheral humaninterface components is a wearable computer, which might include aremote pico projector along with one or more cameras that remotelycommunicate with a separately located client computer to sense a user'sgestures toward portions of an image projected by the pico projectoronto a reflected surface such as a wall or the user's hand.

A client computer may include web browser application 226 that isconfigured to receive and to send web pages, web-based messages,graphics, text, multimedia, and the like. The client computer's browserapplication may employ virtually any programming language, including awireless application protocol messages (WAP), and the like. In one ormore embodiments, the browser application is enabled to employ HandheldDevice Markup Language (HDML), Wireless Markup Language (WML),WMLScript, JavaScript, Standard Generalized Markup Language (SGML),HyperText Markup Language (HTML), eXtensible Markup Language (XML),HTML5, and the like.

Memory 204 may include RAM, ROM, or other types of memory. Memory 204illustrates an example of computer-readable storage media (devices) forstorage of information such as computer-readable instructions, datastructures, program modules or other data. Memory 204 may store BIOS 208for controlling low-level operation of client computer 200. The memorymay also store operating system 206 for controlling the operation ofclient computer 200. It will be appreciated that this component mayinclude a general-purpose operating system such as a version of UNIX, orLINUX™, or a specialized client computer communication operating systemsuch as Windows Phone™, or the Symbian® operating system. The operatingsystem may include, or interface with a Java virtual machine module thatenables control of hardware components or operating system operationsvia Java application programs.

Memory 204 may further include one or more data storage 210, which canbe utilized by client computer 200 to store, among other things,applications 220 or other data. For example, data storage 210 may alsobe employed to store information that describes various capabilities ofclient computer 200. The information may then be provided to anotherdevice or computer based on any of a variety of methods, including beingsent as part of a header during a communication, sent upon request, orthe like. Data storage 210 may also be employed to store socialnetworking information including address books, buddy lists, aliases,user profile information, or the like. Data storage 210 may furtherinclude program code, data, algorithms, and the like, for use by aprocessor, such as processor 202 to execute and perform actions. In oneembodiment, at least some of data storage 210 might also be stored onanother component of client computer 200, including, but not limited to,non-transitory processor-readable removable storage device 236,processor-readable stationary storage device 234, or even external tothe client computer.

Applications 220 may include computer executable instructions which,when executed by client computer 200, transmit, receive, or otherwiseprocess instructions and data. Applications 220 may include, forexample, client user interface engine 222, other client applications224, web browser 226, or the like. Client computers may be arranged toexchange communications one or more servers.

Other examples of application programs include calendars, searchprograms, email client applications, IM applications, SMS applications,Voice Over Internet Protocol (VOIP) applications, contact managers, taskmanagers, transcoders, database programs, word processing programs,security applications, spreadsheet programs, games, search programs,visualization applications, and so forth.

Additionally, in one or more embodiments (not shown in the figures),client computer 200 may include an embedded logic hardware deviceinstead of a CPU, such as, an Application Specific Integrated Circuit(ASIC), Field Programmable Gate Array (FPGA), Programmable Array Logic(PAL), or the like, or combination thereof. The embedded logic hardwaredevice may directly execute its embedded logic to perform actions. Also,in one or more embodiments (not shown in the figures), client computer200 may include one or more hardware micro-controllers instead of CPUs.

In one or more embodiments, the one or more micro-controllers maydirectly execute their own embedded logic to perform actions and accessits own internal memory and its own external Input and Output Interfaces(e.g., hardware pins or wireless transceivers) to perform actions, suchas System On a Chip (SOC), or the like.

Illustrative Network Computer

FIG. 3 shows one embodiment of network computer 300 that may be includedin a system implementing one or more of the various embodiments. Networkcomputer 300 may include many more or less components than those shownin FIG. 3. However, the components shown are sufficient to disclose anillustrative embodiment for practicing these innovations. Networkcomputer 300 may represent, for example, one or more embodiments of afile system management server computer such as file system managementserver computer 116, or the like, of FIG. 1.

Network computers, such as, network computer 300 may include a processor302 that may be in communication with a memory 304 via a bus 328. Insome embodiments, processor 302 may be comprised of one or more hardwareprocessors, or one or more processor cores. In some cases, one or moreof the one or more processors may be specialized processors designed toperform one or more specialized actions, such as, those describedherein. Network computer 300 also includes a power supply 330, networkinterface 332, audio interface 356, display 350, keyboard 352,input/output interface 338, processor-readable stationary storage device334, and processor-readable removable storage device 336. Power supply330 provides power to network computer 300.

Network interface 332 includes circuitry for coupling network computer300 to one or more networks, and is constructed for use with one or morecommunication protocols and technologies including, but not limited to,protocols and technologies that implement any portion of the OpenSystems Interconnection model (OSI model), global system for mobilecommunication (GSM), code division multiple access (CDMA), time divisionmultiple access (TDMA), user datagram protocol (UDP), transmissioncontrol protocol/Internet protocol (TCP/IP), Short Message Service(SMS), Multimedia Messaging Service (MMS), general packet radio service(GPRS), WAP, ultra-wide band (UWB), IEEE 802.16 WorldwideInteroperability for Microwave Access (WiMax), Session InitiationProtocol/Real-time Transport Protocol (SIP/RTP), or any of a variety ofother wired and wireless communication protocols. Network interface 332is sometimes known as a transceiver, transceiving device, or networkinterface card (NIC). Network computer 300 may optionally communicatewith a base station (not shown), or directly with another computer.

Audio interface 356 is arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 356 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others or generate an audio acknowledgment forsome action. A microphone in audio interface 356 can also be used forinput to or control of network computer 300, for example, using voicerecognition.

Display 350 may be a liquid crystal display (LCD), gas plasma,electronic ink, light emitting diode (LED), Organic LED (OLED) or anyother type of light reflective or light transmissive display that can beused with a computer. In some embodiments, display 350 may be a handheldprojector or pico projector capable of projecting an image on a wall orother object.

Network computer 300 may also comprise input/output interface 338 forcommunicating with external devices or computers not shown in FIG. 3.Input/output interface 338 can utilize one or more wired or wirelesscommunication technologies, such as USB™, Firewire™, WiFi, WiMax,Thunderbolt™, Infrared, Bluetooth™, Zigbee™, serial port, parallel port,and the like.

Also, input/output interface 338 may also include one or more sensorsfor determining geolocation information (e.g., GPS), monitoringelectrical power conditions (e.g., voltage sensors, current sensors,frequency sensors, and so on), monitoring weather (e.g., thermostats,barometers, anemometers, humidity detectors, precipitation scales, orthe like), or the like. Sensors may be one or more hardware sensors thatcollect or measure data that is external to network computer 300. Humaninterface components can be physically separate from network computer300, allowing for remote input or output to network computer 300. Forexample, information routed as described here through human interfacecomponents such as display 350 or keyboard 352 can instead be routedthrough the network interface 332 to appropriate human interfacecomponents located elsewhere on the network. Human interface componentsinclude any component that allows the computer to take input from, orsend output to, a human user of a computer. Accordingly, pointingdevices such as mice, styluses, track balls, or the like, maycommunicate through pointing device interface 358 to receive user input.

GPS transceiver 340 can determine the physical coordinates of networkcomputer 300 on the surface of the Earth, which typically outputs alocation as latitude and longitude values. GPS transceiver 340 can alsoemploy other geo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), Enhanced Observed Time Difference(E-OTD), Cell Identifier (CI), Service Area Identifier (SAI), EnhancedTiming Advance (ETA), Base Station Subsystem (BSS), or the like, tofurther determine the physical location of network computer 300 on thesurface of the Earth. It is understood that under different conditions,GPS transceiver 340 can determine a physical location for networkcomputer 300. In one or more embodiments, however, network computer 300may, through other components, provide other information that may beemployed to determine a physical location of the client computer,including for example, a Media Access Control (MAC) address, IP address,and the like.

In at least one of the various embodiments, applications, such as,operating system 306, file system engine 322, control engine 324, webservices 329, or the like, may be arranged to employ geo-locationinformation to select one or more localization features, such as, timezones, languages, currencies, currency formatting, calendar formatting,or the like. Localization features may be used in user interfaces,dashboards, reports, as well as internal processes or databases. In atleast one of the various embodiments, geo-location information used forselecting localization information may be provided by GPS 340. Also, insome embodiments, geolocation information may include informationprovided using one or more geolocation protocols over the networks, suchas, wireless network 108 or network 111.

Memory 304 may include Random Access Memory (RAM), Read-Only Memory(ROM), or other types of memory. Memory 304 illustrates an example ofcomputer-readable storage media (devices) for storage of informationsuch as computer-readable instructions, data structures, program modulesor other data. Memory 304 stores a basic input/output system (BIOS) 308for controlling low-level operation of network computer 300. The memoryalso stores an operating system 306 for controlling the operation ofnetwork computer 300. It will be appreciated that this component mayinclude a general-purpose operating system such as a version of UNIX, orLINUX™, or a specialized operating system such as MicrosoftCorporation's Windows® operating system, or Apple Corporation's OSX®operating system. The operating system may include, or interface withone or more virtual machine modules, such as, a Java virtual machinemodule that enables control of hardware components or operating systemoperations via Java application programs. Likewise, other runtimeenvironments may be included.

Memory 304 may further include one or more data storage 310, which canbe utilized by network computer 300 to store, among other things,applications 320 or other data. For example, data storage 310 may alsobe employed to store information that describes various capabilities ofnetwork computer 300. The information may then be provided to anotherdevice or computer based on any of a variety of methods, including beingsent as part of a header during a communication, sent upon request, orthe like. Data storage 310 may also be employed to store socialnetworking information including address books, friend lists, aliases,user profile information, or the like. Data storage 310 may furtherinclude program code, data, algorithms, and the like, for use by aprocessor, such as processor 302 to execute and perform actions such asthose actions described below. In one embodiment, at least some of datastorage 310 might also be stored on another component of networkcomputer 300, including, but not limited to, non-transitory media insideprocessor-readable removable storage device 336, processor-readablestationary storage device 334, or any other computer-readable storagedevice within network computer 300, or even external to network computer300. Data storage 310 may include, for example, file storage 314, filesystem data 316, control models 318, or the like.

Applications 320 may include computer executable instructions which,when executed by network computer 300, transmit, receive, or otherwiseprocess messages (e.g., SMS, Multimedia Messaging Service (MMS), InstantMessage (IM), email, or other messages), audio, video, and enabletelecommunication with another user of another mobile computer. Otherexamples of application programs include calendars, search programs,email client applications, IM applications, SMS applications, Voice OverInternet Protocol (VOIP) applications, contact managers, task managers,transcoders, database programs, word processing programs, securityapplications, spreadsheet programs, games, search programs, and soforth. Applications 320 may include file system engine 322, controlengine 324, web services 329, or the like, that may be arranged toperform actions for embodiments described below. In one or more of thevarious embodiments, one or more of the applications may be implementedas modules or components of another application. Further, in one or moreof the various embodiments, applications may be implemented as operatingsystem extensions, modules, plugins, or the like.

Furthermore, in one or more of the various embodiments, file systemengine 322, control engine 324, web services 329, or the like, may beoperative in a cloud-based computing environment. In one or more of thevarious embodiments, these applications, and others, that comprise themanagement platform may be executing within virtual machines or virtualservers that may be managed in a cloud-based based computingenvironment. In one or more of the various embodiments, in this contextthe applications may flow from one physical network computer within thecloud-based environment to another depending on performance and scalingconsiderations automatically managed by the cloud computing environment.Likewise, in one or more of the various embodiments, virtual machines orvirtual servers dedicated to file system engine 322, control engine 324,web services 329, or the like, may be provisioned and de-commissionedautomatically.

Also, in one or more of the various embodiments, file system engine 322,control engine 324, web services 329, or the like, may be located invirtual servers running in a cloud-based computing environment ratherthan being tied to one or more specific physical network computers.

Further, network computer 300 may also comprise hardware security module(HSM) 360 for providing additional tamper resistant safeguards forgenerating, storing or using security/cryptographic information such as,keys, digital certificates, passwords, passphrases, two-factorauthentication information, or the like. In some embodiments, hardwaresecurity module may be employed to support one or more standard publickey infrastructures (PKI), and may be employed to generate, manage, orstore key pairs, or the like. In some embodiments, HSM 360 may be astand-alone network computer, in other cases, HSM 360 may be arranged asa hardware card that may be installed in a network computer.

Additionally, in one or more embodiments (not shown in the figures),network computer 300 may include an embedded logic hardware deviceinstead of a CPU, such as, an Application Specific Integrated Circuit(ASIC), Field Programmable Gate Array (FPGA), Programmable Array Logic(PAL), or the like, or combination thereof. The embedded logic hardwaredevice may directly execute its embedded logic to perform actions. Also,in one or more embodiments (not shown in the figures), the networkcomputer may include one or more hardware microcontrollers instead of aCPU. In one or more embodiments, the one or more microcontrollers maydirectly execute their own embedded logic to perform actions and accesstheir own internal memory and their own external Input and OutputInterfaces (e.g., hardware pins or wireless transceivers) to performactions, such as System On a Chip (SOC), or the like.

Illustrative Logical System Architecture

FIG. 4 illustrates a logical architecture of file system 400 formanaging throughput fairness and quality of service in file systems inaccordance with one or more of the various embodiments. In at least oneof the various embodiments, file system 400 may be comprised of a filesystem management server computer, such as, file system managementserver computer 402, as well as, one or more storage computers, such as,storage computer 404, storage computer 406, storage computer 408, or thelike. In at least one of the various embodiments, each computer may beinterconnected over a network, such as, network 410. In at least one ofthe various embodiments, network 410 may be considered to be arranged tobe similar to wireless network 108 or network 110.

In at least one of the various embodiments, the storage computers may bearranged to include one or more storage devices, such as, storagedevices 412, storage devices 414, or storage devices 416. In variousembodiments, storage computers may include more or fewer storage devicesthan illustrated in FIG. 4. In at least one of the various embodiments,storage computers may include a single storage device. And, in someembodiments, one or more storage computers may be arranged to beincluded in an enclosure or chassis which in turn may be interconnectedto other computers and/or storage computers over network 410.

In one or more of the various embodiments, storage computers may beemployed to provide a file system object store for storing the filesystem objects that contain or represent the information stored in filesystem 400.

In at least one of the various embodiments, the functionality of filesystem management server computer 402 may be incorporated directly intoone or more storage computers, such as, storage computer 404, storagecomputer 406, storage computer 408, or the like. In such embodiments afile system engine, such as, file system engine 322 may be operative onone or more of the storage computers.

In one or more of the various embodiments, the implementation detailsthat enable file system 402 to operate may be hidden from clients, suchthat they may be arranged to use file system 402 the same way they useother conventional file systems, including local file systems.Accordingly, in one or more of the various embodiments, clients may beunaware that they are using a distributed file system that supportspredictive performance analysis because file system engines may bearranged to provide an interface or behavior that may be similar to oneor more standard file systems.

Also, while file system 400 is illustrated as using one file systemmanagement computer, the innovations are not so limited. Innovationsherein contemplate file systems that include two or more file systemmanagement computers or one or more file system object data stores. Insome embodiments, file system object stores may be located remotely fromone or more file system management computers. Also, a logical filesystem object store or file system may be spread across two or morecloud computing environments, storage clusters, or the like.

FIG. 5 illustrates a logical schematic of system 500 for managingthroughput fairness and quality of service in file systems in accordancewith one or more of the various embodiments. In this example, for someembodiments, system 500 includes a storage device, such as, storagedevice 502. In this example, storage device 502 represents a device,such as, a hard drive. For brevity or clarity, various components commonto hard drives, or the like, such as, platters, spindles, read-writearm(s), actuator motor(s), control interfaces, data buses, memory,firmware, power regulators, or the like, are omitted here. One ofordinary skill in the art will appreciate that such elements, or thelike, may be included in a storage device. Further, in one or more ofthe various embodiments, other innovations described herein are notlimited to hard-drive storage devices. One of ordinary skill in the artwill appreciate that these innovations anticipate other block storagedevices, file storage devices, object storage devices, or the like, orcombination thereof.

In one or more of the various embodiments, storage devices, such as,storage device 502 may be arranged to support one or more commandprotocols or one or more data protocols, such as, Serial AdvancedTechnology Attachment (SATA), Advance Host Controller Interface (AHCI),custom protocols, or the like, that enable storage computers to performvarious actions related to using the storage device in a file system,including, reading data, writing data, flushing buffers, identifyingdevices, checking device status, or the like. In some embodiments,storage devices may be arranged to accept a finite number of commands atgiven time. In some embodiments, the configuration/content/format ofcommands may vary depending on the protocol used or the make or model ofthe device. In some embodiments, if more than one command may beprovided to a storage device, those commands may be executed in thesequence they are provided to the storage device.

For example, in some embodiments, if twenty read commands are providedto a storage device, in some cases (depending on the type of device orprotocol), all twenty commands may be executed by the storage device inthe order they are provided. Thus, in some embodiments, in this example,if twenty low priority commands are sent to a storage device, a laterarriving high priority command cannot execute until the first twenty lowpriority commands may be executed. Accordingly, in some embodiments, afile system may provide a scheduler, such as, scheduler 504 to determinewhich commands may be sent to storage device 502.

In one or more of the various embodiments, system 500 may be arranged toinclude one or more client controllers, such as, client controller 506,client controller 508, client controller 510, or the like. In someembodiments, client controllers may be arranged to provide signals thatschedulers may employ to determine the commands to provide to storagedevice 502.

In one or more of the various embodiments, client jobs, such as, clientjob 512, client job 514, client job 516, or the like, represent filesystem tasks that file system may be performing on behalf of variousclients of the file system.

In one or more of the various embodiments, one or more client jobs maybe maintenance jobs associated with the operation or support of the filesystem. For example, in some embodiments, these may include replicationjobs, recovery jobs, re-striping jobs, or the like, that may beresponsible for maintaining stability, availability, or correctness of adistributed file system.

Also, in one or more of the various embodiments, one or more client jobsmay be associated with regular (human) users that may be interactingwith the file system. For example, in some embodiments, such client jobsmay include users browsing a file directory, opening a file, saving afile, or the like.

In one or more of the various embodiments, control engines may bearranged to provide one or more client controllers to monitor the statusor performance of client jobs to help ensure that the file systemmaintains a level of quality of service (e.g., QoS) required for a givenjob. Accordingly, in one or more of the various embodiments, each clientjob may be associated with its own client controller. Thus, in someembodiments, the status of each individual may be determinedindependently.

In one or more of the various embodiments, client controllers may bearranged to employ control models selected from control models 518 todetermine if a client job may be running on schedule. Accordingly, inone or more of the various embodiments, each client controller (orclient job) may be associated with a control model that determines ifthe client job may be on schedule.

In one or more of the various embodiments, file systems may be arrangedto provide different types of control models that may be associated withdifferent types of client jobs. Accordingly, in one or more of thevarious embodiments, performance criteria, metrics, or the like, may betailored to the characteristics or requirements of the different jobs orjob types.

In one or more of the various embodiments, the different types ofcontrol models may be arranged to output a target score that ascheduler, such as, scheduler 504 may employ to allocate or select jobcommands for execution on the storage device.

In one or more of the various embodiments, control models may bearranged to provide a target score that indicates if its associatedclient job may be running ahead of schedule, on schedule, or behindschedule. For example, in some embodiments, if the target score is apositive value, the job may be considered to be ahead of schedule. Incontrast, in some embodiments, if the score is a negative value, the jobmay be considered to be behind schedule. Note, in some embodiments, the‘meaning’ of ahead or behind or schedule, may be dependent on theparticular client job as determined by its associated control model.

However, in some embodiments, scheduler 504 may be arranged to assume ifa job target score is positive, the job may be slowed withoutconsequence. Similarly, in some embodiments, if a target score isnegative, the job will require speeding up to complete on-time.

Further, in one or more of the various embodiments, the magnitude of atarget score may be employed to compare pending client jobs against eachother. Accordingly, in some embodiments, a client job associated with ahigher magnitude score may be assumed to be farther ahead or fartherbehind of a job associated with a score with a lower magnitude. Forexample, in one or more of the various embodiments, if job A has atarget score of 1000 and job B has a target score of 100, job A may beconsidered to be further ahead than job B. Similarly, for example, ifjob X has a target score of −1000 and job Y has a target score of −100,job X may be considered to be further behind than job Y.

In one or more of the various embodiments, schedulers may be enabled tospeed up or slow down client jobs by adjusting the number job commandsexecuted for a given client job. For example, in some embodiments,executing more job commands than one at a time may be more efficient fora hard drive storage device, because it may reduce seeks if the jobcommands may be related to data locations that may be contiguous on thephysical media. Accordingly, in some embodiments, schedulers may enablea behind-schedule job to catch up if more job commands for that job maybe executed because doing so may it improve hard drive efficiency.

In one or more of the various embodiments, control models 518 may be adata store that includes one or more control models associated withclient jobs. In one or more of the various embodiments, controllerengines, or the like, may be arranged to evaluate meta-data associatedwith incoming jobs to assign each incoming job a control model. In oneor more of the various embodiments, control engines may be arranged toemploy rules, instructions, lookup tables, or the like, provided viaconfiguration information to determine which control models may beassociated with a client jobs.

In one or more of the various embodiments, control engines may bearranged to monitor or otherwise measure the accuracy or efficiency ofcontrol models. Accordingly, in some embodiments, control models may begraded based on various performance criteria. In some embodiments, ifthere may be two or more control models for the same client job type,they may be automatically compared to determine if one may be betterthan another.

FIG. 6 illustrates a logical schematic of client job 600 for managingthroughput fairness and quality of service in file systems in accordancewith one or more of the various embodiments. As described above, in someembodiments, client jobs may be provided to a controller engine that mayassociate client jobs with control models.

In one or more of the various embodiments, client jobs may be comprisedof one or more components, such as, job envelope 602, job meta-data 604,job commands 606, or the like. In one or more of the variousembodiments, job envelopes may be considered a data structure that mayencapsulate different portions of a client job. For example, in someembodiments, job envelopes may be classes, structures, tuples, objects,records, or the like, that enable job meta-data and job commands to beassociated.

In one or more of the various embodiments, job meta-data, such as, jobmeta-data 604 may be arranged to include various information associatedwith a job, such as, job type, job owner, job size, job status, or thelike. In some embodiments, the particular meta-data included with a jobmay vary depending on the type of job, or the like. In some embodiments,the control models for a particular job type may require particularmeta-data. Accordingly, the job meta-data may be arranged to conform tothe requirements of one or more control models.

In one or more of the various embodiments, job commands, such as, jobcommands 606 may be a sequence of one or more commands that comprise thejob. In some embodiments, the job commands may be low level commandsthat may be directly executed or interpreted by storage devices orstorage device drivers. Alternatively, in some embodiments, job commandsmay be defined using a conventional computer language or a domainspecific language. In some embodiments, job commands may be arranged toimplement the file system operations associated with client jobs. Forexample, in some embodiments, job commands may contain a list of SATA orAHCI command codes for a particular storage device. Also, for example,in some embodiments, job commands may include higher level commands thatmay be translated or compiled onto lower level SATA or AHCI commands atruntime.

FIG. 7 illustrates a logical schematic of control model 700 for managingthroughput fairness and quality of service in file systems in accordancewith one or more of the various embodiments. In one or more of thevarious embodiments, controller engines may be arranged to employcontrol models to provide a scheduler information (target scores) todetermine which client job commands to execute. Accordingly, in someembodiments, as client jobs are provided to a storage computer, theclient jobs may be associated with a control model.

In one or more of the various embodiments, client models may be arrangedto produce target scores comprised of one or more dimensionless scalarvalues that correspond to the performance or completion status of aclient job. In some embodiments, the associated performance criteria,such as, latency, time remaining, timeouts, outstanding work, throughpututilization, or the like, may vary depending on the particular controlmodel. However, in some embodiments, the target scores produced bycontrol models may be arranged such that target scores from differentcontrol models may be compared independently of the criteria used by thecontrol model that generated the target scores.

In one or more of the various embodiments, target scores provided bycontrol models may be employed by a scheduler to control the ‘speed’ ofjobs. As described above, in some embodiments, jobs associated withpositive target score may be assumed to be behind schedule as defined byits associated control model. Similarly, in some embodiments, jobsassociated with a negative target score may be assumed to be ahead ofschedule as defined by its associated control model. Also, in someembodiments, jobs associated with a target score of zero (0) may beassumed to be on schedule as defined by its associated control model.

Also, in one or more of the various embodiments, how far ahead ofschedule or how far behind schedule a job may be represented by themagnitude of a target score. For example, in some embodiments, if Job Ahas a target score of 100 and Job B has a target score of 200, thescheduler may be arranged to consider Job B farther behind that Job A.

In this example, for some embodiments, control model 700 represents aninstance of a control model. In some embodiments, as described above,different types of control models may be employed for different types ofjobs or different operational requirements of a given file system.Accordingly, in some embodiments, this example (control model 700) ispresented as an example of a type of control model that may be used inthe innovations disclosed herein. One of ordinary skill in the art willappreciate that these innovations contemplate other control models thatmay be employed depending on local circumstances or requirements of agiven file system.

In this example, for some embodiments, control model 700 may be arrangedto receive client jobs or client job information at an interface, suchas, input interface 702. In some embodiments, this information may beprovided via the job meta-data associated with a provided job. In someembodiments, accumulator 704 may be arranged to compute an error valuethat represents the difference between the job target and the one ormore metrics provided via job metrics 714. In some embodiments, theerror value may be measured based on the comparison of one or moremetrics associated with the current job and a setpoint value that may bedefined.

In some embodiments, the control model may be arranged to generate acorrection value that may be based on proportional error engine 706,integral error engine 708, and derivative error engine 710. In thisexample, for some embodiments, proportional error engine 706 may bearranged to generate a partial target score value that may beproportional to the magnitude of the error value. In this example, forsome embodiments, integral error engine 708 may be arranged to generatea partial target score value representing the historic cumulative errorassociated with the running job. Also, in this example, for someembodiments, derivative error engine 712 may be arranged to generate apartial target score value based on the current rate of change of theerror value.

Accordingly, in this example, for some embodiments, the partial targetscore value may be summed by accumulator 712 to provide the target scorefor the client job. In some embodiments, control engines may be arrangedto provide the target score to scheduler 716 that may determine whichjob commands should be executed by a storage device.

In one or more of the various embodiments, the particular functionsexecuted by accumulator 704, proportional error engine 706, integralerror engine 708, derivative error engine 710, accumulator 712 may bespecific to a particular control model enabling control behavior to betailored for different types of client jobs.

Likewise, in some embodiments, setpoint values may be provided by afunction that includes various inputs such as time (t), or the like. Insome embodiments, such functions may be different for different controlmodels.

Also, in one or more of the various embodiments, job metrics 714 may bebased on various metrics or functions of metrics that may be differentfor different control models. For example, in some embodiments, keymetrics may include, remaining work (data to read or write), currentthroughput, number of jobs competing for the same resource, timeremaining before a deadline or timeout may be reached, or the like.

Accordingly, in some embodiments, control models may be arranged toemploy a function to generate a job status value based on the one ormore metrics that may be compared to the current setpoint value for aclient job.

As described above, in some embodiments, different types of controlmodels may be employed for different types of jobs. For example, in someembodiments, some job types may be associated control models that may belinear, non-linear, constant, or the like.

In one or more of the various embodiments, for example, one or moreclient jobs associated with interactive clients (e.g., users) may beautomatically assigned a constant target score of zero rather than avariable target score. Thus, in this example, other jobs that may havepositive target values (e.g., behind schedule) may be given priority.However, in this example, for some embodiments, interactive users may begranted higher priority over jobs that may be ahead of schedule.

In one or more of the various embodiments, one or more control modelsmay be based on machine learning models that employ one or morenon-linear mechanisms to provide a target score given inputs or signalsassociated with a client job.

Further, in one or more of the various embodiments, one or more controlmodels may be non-linear such that target scores may be discrete ratherthan continuous. For example, in some embodiments, a control model maybe arranged to produce target scores of +100, 0, −100 depending on jobmetrics.

In one or more of the various embodiments, schedulers, such as scheduler716 may be arranged to enable select job command for execution on astorage device based on a comparison of the target scores. Accordingly,in some embodiments, control models may be employed for scheduling aslong as they produce a target score that a scheduler may compare againsttarget scores provided by other control models.

Generalized Operations

FIGS. 8-12 represent generalized operations for predictive performanceanalysis for file systems in accordance with one or more of the variousembodiments. In one or more of the various embodiments, processes 800,900, 1000, 1100, and 1200 described in conjunction with FIGS. 8-12 maybe implemented by or executed by one or more processors on a singlenetwork computer, such as network computer 300 of FIG. 3. In otherembodiments, these processes, or portions thereof, may be implemented byor executed on a plurality of network computers, such as networkcomputer 300 of FIG. 3. In yet other embodiments, these processes, orportions thereof, may be implemented by or executed on one or morevirtualized computers, such as, those in a cloud-based environment.However, embodiments are not so limited and various combinations ofnetwork computers, client computers, or the like may be utilized.Further, in one or more of the various embodiments, the processesdescribed in conjunction with FIGS. 8-12 may perform actions formanaging throughput fairness and quality of service in file systems inaccordance with at least one of the various embodiments or architecturessuch as those described in conjunction with FIGS. 4-7. Further, in oneor more of the various embodiments, some or all of the actions performedby processes 800, 900, 1000, 1100, and 1200 may be executed in part byfile system engine 322, control engine 324, or the like.

FIG. 8 illustrates an overview flowchart for process 800 for managingthroughput fairness and quality of service in file systems in accordancewith one or more of the various embodiments. After a start block, atdecision block 802, in one or more of the various embodiments, if one ormore client jobs may be available, control may flow to block 804;otherwise, control may loop back decision block 802. In one or more ofthe various embodiments, as file system clients, such as, users,services, maintenance processes, file system support processes, or thelike, perform actions that require the file system, client jobs may begenerated and distributed to storage computers (e.g., nodes) in the filesystem.

At block 804, in one or more of the various embodiments, control enginesmay be arranged to select one or more client job commands based ontarget scores associated with the one or more client jobs. As describedabove, client jobs may be associated with control models that may beemployed to generate target scores. Accordingly, in one or more of thevarious embodiments, the target scores may be employed to select jobcommands for execution. As described above, in one or more of thevarious embodiments, client jobs associated with positive target scoresmay be considered jobs that may be behind schedule. Accordingly, in someembodiments, schedulers associated with the control engines may bearranged to preferentially select job commands for behind-schedule jobsover client jobs that may be ahead of schedule or on schedule. In someembodiments, the particular number of job commands to select ordetermining if job commands from more than one client job may beselected may vary depending on how a control engine or scheduler may beconfigured.

In one or more of the various embodiments, there may be one client jobpending at a storage computer. Accordingly, in some embodiments,schedulers may be arranged to select at least one job command from thepending client job. Accordingly, in some embodiments, even if the singleclient job may be ahead of schedule, the scheduler may still select atleast one job command for that client job.

At block 806, in one or more of the various embodiments, control enginesmay be arranged to execute one or more client job commands for the oneor more client jobs. In some embodiments, the one or more selected jobcommands may be provided to a storage device for execution.

At block 808, in one or more of the various embodiments, control enginesmay be arranged to update the one or more target scores for the one ormore client jobs. In one or more of the various embodiments, controlengines may be arranged to provide one or more metric values associatedwith the execution of the client jobs to the control models associatedwith each client job. Accordingly, in one or more of the variousembodiments, the target scores associated with the client jobs may beupdated by their respective control models.

Next, in one or more of the various embodiments, control may be returnedto a calling process.

FIG. 9 illustrates a flowchart for process 900 for managing throughputfairness and quality of service in file systems in accordance with oneor more of the various embodiments. After a start block, at block 902,in one or more of the various embodiments, one or more client jobs maybe provided to a control engine. As described above, in someembodiments, various clients, including users, services, maintenanceprocesses, file system support processes, or the like, or combinationthereof, may be performing tasks that may require accessing storagedevices in the file system. Accordingly, in one or more of the variousembodiments, client jobs may be provided to distribute the tasks acrossthe file system to various nodes or storage computers to perform thevarious actions associated with the required tasks.

In one or more of the various embodiments, the distribution of theclient jobs may be managed by higher level systems that may rely on filesystem facilities, such as, path strings, indexes, file system datastructures, or the like, to determine the storage devices for servicinga particular client job. Thus, in some embodiments, the client job maybe assumed to include or be associated with one or more job commandsthat may be serviced by the one or more storage devices managed by thecontrol engine. In some embodiments, other control engines that may bemanaging other storage devices may be managing other portions of alarger set of tasks that a client job may be part of.

At block 904, in one or more of the various embodiments, the controlengines may be arranged to associate a control model with each of theone or more client jobs. In one or more of the various embodiments,control engines may be arranged to employ meta-data (e.g., jobmeta-data) that may be included with or associated with client jobs todetermine the control models for each individual client job.

In some cases, in some embodiments, the same control model type may beassociated with different client jobs. Thus, in one or more of thevarious embodiments, each client job may be assumed to be associatedwith an individual instance of a control model of a given type. However,for brevity and clarity, the term ‘control model’ is employedinterchangeably to describe different types of control models ordifferent instances of control models depending on the context.

In one or more of the various embodiments, control engines may bearranged to associate particular client job types to particular controlmodels. For example, in some embodiments, interactive user jobs may beassociated with one type of control model while longer running supportjobs may be associated with another type of control model. In one ormore of the various embodiments, control models may be associated withclient jobs based on a variety of factors, including, user role,process/task priority, type of support process (e.g., re-striping vs.replication), application type (e.g., streaming media, database queries,or the like), time of day, file system utilization metrics, or the like.Accordingly, in one or more of the various embodiments, control enginesmay be arranged to employ mapping rules, lookup tables, or the like,provided via configuration information to account for localcircumstances or local requirements.

In one or more of the various embodiments, control engines may bearranged to employ some or all client job metadata or the client jobcharacteristics as inputs to one or more machine learning models thatmay be trained or configured to select control models to associate withincoming client jobs.

At block 906, in one or more of the various embodiments, the controlengines may be arranged to determine target scores for each client jobbased on the control model associated with each client job. In one ormore of the various embodiments, target scores generated by controlmodels may be based on various criteria. In some embodiments, differentcontrol models may be arranged to have different criteria. In someembodiments, some or all criteria may be provided to control engines asclient job metadata. In other cases, for some embodiments, criteria maybe defined in the control model.

In one or more of the various embodiments, a control model may defineone or more metrics thresholds or ranges that on-schedule client jobsshould meet. Thus, in some embodiments, if the client job metrics meetthe thresholds or ranges, the job may be considered on schedule;otherwise, the job may be considered behind schedule or ahead ofschedule.

In one or more of the various embodiments, control models may bearranged to generate target scores that may be compared or measuredagainst target scores provided by other control models. Accordingly, insome embodiments, control engines may be arranged to provide targetscores that may be dimensionless and normalized such that if the sametarget score value is provided by two or more control models thecorresponding client jobs may be considered to be performing similarly.

However, in one or more of the various embodiments, control models maybe arranged to employ different methods to produce target scores suchthat ahead-of-schedule, on-schedule, or behind-schedule may havedifferent meanings for different client jobs. Likewise, in one or moreof the various embodiments, different control models may be arranged toemploy different metrics to generate target scores. For example, in someembodiments, control model A may be arranged to use the amount of datato be read as a scheduling metric where control model B may be arrangedto employ a running average of data transfer rate.

For example, in some embodiments, a control model may be arranged toestablish a setpoint value that may be compared to a measured or derivedmetric value associated with its associated pending client job.Accordingly, for example, the control model may generate a target scorebased on the difference between the setpoint value and themeasured/derived metric value to compute an error value. Thus, forexample, a positive error value may result in the control modelproviding a positive target score that has a magnitude proportional tothe magnitude of the error value. Similarly, for example, if the errorvalue is a negative value, the target score may be a negative valueindicating that the client job may be ahead of schedule.

In some embodiments, one or more control models may be arranged tooutput a constant target score for a given client job type. For example,in some embodiments, if control models may be arranged to always producea target score of zero (0), the scheduler may assume that the job is onschedule. Thus, in this example, the scheduler will prioritizebehind-schedule jobs and de-prioritize ahead-of-schedule jobs withrespect to the jobs pinned to a target score of zero.

At block 908, in one or more of the various embodiments, schedulers maybe arranged to select one or more job commands for execution based onthe target scores. In one or more of the various embodiments, schedulersmay be provided to the one or more target scores produced by the controlmodels. Accordingly, in some embodiments, scheduler may be arranged toallocate or select job commands associated with client jobs based on acomparison or ranking of the target scores.

In some embodiments, storage devices may be enabled to accept more thanone job command at a time. For example, in some embodiments, somestorage devices (e.g., SATA/AHCI devices) may be arranged to accept upto 32 job commands at a time that are executed in the order they areprovided to the storage device.

Accordingly, in some embodiments, schedulers may be arranged to selectone or more job commands from one or more client jobs for execution onthe storage device. In some embodiments, schedulers may be arranged toselect more job commands for client jobs that may be behind schedulethan commands for jobs that may be on schedule or ahead of schedule. Insome embodiments, schedulers may be arranged to proportionally allocatethe number of commands selected per job based on the relative magnitudeof the target scores.

In some embodiments, schedulers may be arranged to sort pending clientjobs into an ordered list based on their associated target scores. Insome embodiments, the ordered list may be employed to allocate jobcommands. Accordingly, in one or more of the various embodiments, clientjobs that may be farthest behind schedule (e.g., largest positive targetscore) may be allocated the most commands while client jobs that may beless behind schedule may be allocated fewer job commands to run. And, insome embodiments, client jobs ahead of schedule or on schedule may beallocated few or no job commands.

In some embodiments, if all client jobs may be ahead of schedule, thescheduler may be arranged to allocate some job commands to keep thestorage device busy (e.g., utilized) even though the pending jobs may beahead of schedule.

In one or more of the various embodiments, schedulers may be arranged torestrict the number of job commands sent to storage devices to a limitless than the command capacity of the storage device. In one or more ofthe various embodiments, limiting the number of job commands sent at onetime may reduce the time it may take for all of the commands tocomplete. In some cases, this may be advantageous because it may reducethe amount of time an incoming higher priority client job may have towait while previously sent commands associated with lower priorityclient jobs execute. In one or more of the various embodiments, thecommand limit may vary depending on the size of the command buffer of agiven storage device. Accordingly, in one or more of the variousembodiments, the scheduler may be arranged to determine the commandlimit based on configuration information.

Note, in some embodiments, schedulers may be part of a control engine.In other embodiments, schedulers may be separate processes or schedulingengines.

At block 910, in one or more of the various embodiments, the schedulermay be arranged to provide the one or more selected job commands to astorage device. In one or more of the various embodiments, the selectedjob commands may be sent to the storage device in the determined order.Accordingly, in one or more of the various embodiments, the storagedevice may execute the commands as directed. Note, the particularactions performed for a given job command may vary depending on thestorage device or its command protocol and may be assumed to be beyondthe scope of the control engines or schedulers.

At block 912, in one or more of the various embodiments, the controlengines may be arranged to collect one or more metrics associated withthe one or more client jobs. In one or more of the various embodiments,control models associated with client jobs may be arranged to monitorone or more metrics associated with the performance of associated clientjobs. In one or more of the various embodiments, the particular metrics(if any) may be collected for use in determining an updated target scorefor the running client jobs.

At decision block 914, in one or more of the various embodiments, ifthere may be more client jobs, control may loop back to block 904;otherwise, control may be returned to a calling process.

FIG. 10 illustrates a flowchart for process 1000 for using a controlmodel for managing throughput fairness and quality of service in filesystems in accordance with one or more of the various embodiments. Aftera start block, at block 1002, in one or more of the various embodiments,a client job may be provided to a control engine.

At block 1004, in one or more of the various embodiments, the controlmodel may be arranged to determine a setpoint value and a job statusvalue associated with the client job. In one or more of the variousembodiments, the setpoint value may be defined in the control model. Insome embodiments, the setpoint value may be provided with the jobmeta-data. Also, in some embodiments, the setpoint value may bedetermined based on configuration information.

Also, in one or more of the various embodiments, the job status valuesmay be determined or derived from one or more metrics associated withthe one or more the client jobs or the execution of the client job.

At block 1006, in one or more of the various embodiments, the controlmodel may be arranged to generate an error value based on the setpointvalue and the job status value. For example, in some embodiments, theerror value may be determined by subtracting the job status values fromthe setpoint value.

At block 1008, in one or more of the various embodiments, the controlmodel may be arranged to generate a target score based on the errorvalue. As described for control model 700 in FIG. 7, one or more errorengines included in the control model may be arranged to generatepartial target scores based on the error value. In some embodiments,error values associated with a client job falling behind schedule mayresult in an increased target score; error values associated with aclient job becoming ahead of schedule may result in a decreased targetscore; and error values associated with a client job that may be onschedule may result in a target score moving closer to zero (0).

At decision block 1010, in one or more of the various embodiments, ifthere may be more than one client job, control may flow to block 1012;otherwise, control may flow block 1014.

At block 1012, in one or more of the various embodiments, a schedulermay be arranged to determine one or more job commands based on thetarget scores. As described above, in some embodiments, schedulers maybe configured to select one or more job commands for one or more clientjobs to execute on storage device. Accordingly, in some embodiments,schedulers may select which client job to service based on comparing thetarget scores associated with each client job.

In one or more of the various embodiments, scheduler may be arranged todetermine more job commands for client jobs that may be falling behindschedule than other client jobs that may be ahead of schedule or onschedule. As described above, in some embodiments, the scheduler mayemploy the sign of a target score to determine if the client job isahead of schedule or behind schedule and the magnitude of the targetscore to determine how far a client job a may be ahead or behindschedule.

At block 1014, in one or more of the various embodiments, the schedulermay be arranged to execute the one or more determined job commands. Asdescribed above, in some embodiments, the scheduler may provide thedetermined job commands to the storage device for execution.

At decision block 1016, in one or more of the various embodiments, ifthere may be more client jobs or more job commands, control may loopback to block 1004; otherwise, control may be returned to a callingprocess.

FIG. 11 illustrates a flowchart for process 1100 for managing throughputfairness and quality of service in file systems in accordance with oneor more of the various embodiments. After a start block, at decisionblock 1102, in one or more of the various embodiments, if there may beone client job, control may flow to decision block 1104; otherwise,control may be returned to a calling process. Accordingly, in one ormore of the various embodiments, control engines may be arranged toperform differently if there may be one client job.

In one or more of the various embodiments, similar to multi job cases,control engines may be arranged to determine a control model andassociate it with the client job based on one or more characteristics ofthe client job. Accordingly, in some embodiments, control engines may bearranged to monitor the performance of the client job to determine ifthe client job is on schedule based on the target score produced by thecontrol model.

At decision block 1104, in one or more of the various embodiments, ifthe client job may be behind schedule, control may flow to block 1106;otherwise, control may flow to block 1108. As described above, in someembodiments, control engines may be arranged to determine if a clientjob is on schedule based on the target score provided by the controlmodel.

At block 1106, in one or more of the various embodiments, a schedulermay be arranged to increase the command allocation for the client job.As described above, if a client job may be behind schedule, schedulersmay be arranged to execute more job commands for that client job in anattempt to get the client job back on schedule. Accordingly, in one ormore of the various embodiments, one or more job commands associatedwith a behind schedule job may be selected for execution. In someembodiments, schedulers may be arranged to select a maximum number ofjob commands to send to the storage device to help the client job becomeon schedule.

In one or more of the various embodiments, the number of commands may belimited by the command protocol or storage device interfacecharacteristics. For example, in some embodiments, the command protocol(e.g., SATA) may enable 32 commands to be sent at a time to a storagedevice. However, in some embodiments, schedulers may be arranged toestablish a command limit that may be less than the maximum numbersupported. Accordingly, in some embodiments, schedulers may determinethe maximum number of commands based on configuration information toaccount for local circumstances or local requirements. For example, forsome embodiments, a scheduler may be configured send a maximum of 16commands at a time for a given job to a storage device even though itmay accommodate 32 commands.

Further, in one or more of the various embodiments, schedulers may bearranged to determine the number of commands to send based on themagnitude of the target score. Thus, in some embodiments, more jobcommands may be executed for client jobs that may be farther behindschedule than client jobs that may be less behind schedule. In someembodiments, schedulers may be arranged to determine the particularcommand selection strategy based on configuration information.

At block 1108, in one or more of the various embodiments, the schedulermay be arranged to allocate one command for execution. In one or more ofthe various embodiments, even though the client job may be ahead ofschedule, its commands may still be executed rather than idling orotherwise suspending the job until it is no longer ahead of schedule.

However, in some embodiments, because the client job may be ahead ofschedule, there may be no need to execute it faster by sending more thanone command to the storage device. In one or more of the variousembodiments, this behavior may be advantageous to avoid causing asubsequent higher priority client job from being delayed because it hasto wait for several commands of an ahead-of-schedule or lower priorityclient job to complete before the newly arrived client job can beginexecuting. Thus, in some embodiments, the ahead-of-schedule job maycontinue to execute towards completion while not causing a long delay ifa higher priority client job is provided to the same storage device.

At block 1110, in one or more of the various embodiments, the schedulermay be arranged to execute the client job commands. As described above,in some embodiments, the scheduler may provide the one or more jobcommands to the storage device for execution.

At block 1112, in one or more of the various embodiments, the controlengine may be arranged to update the target score for the client job. Inone or more of the various embodiments, if there may one client job, thecontrol engines may be arranged to continue monitoring its completionmetrics to employ the control model associated with the client job togenerate target scores. Accordingly, in some embodiments, the schedulermay be informed if the client job may be ahead of schedule, on schedule,or behind schedule.

In some embodiments, if there may be more than one client job, eachcontrol model for each client job provides a target score as describedabove.

Next, in one or more of the various embodiments, control may be returnedto a calling process.

FIG. 12 illustrates a flowchart for process 1200 for evaluating controlmodels for managing throughput fairness and quality of service in filesystems in accordance with one or more of the various embodiments. Aftera start block, at decision block 1202, in one or more of the variousembodiments, one or more client jobs may be provided to a controlengine. As described above, a file system management server computer maydistribute one or more client jobs to various nodes in a file system.

At block 1204, in one or more of the various embodiments, the controlengine may be arranged to determine control models for the one or moreclient jobs. As described above, control engines may be arranged toassociate the one or more client jobs with control models based on oneor more characteristics of each client job.

In one or more of the various embodiments, there may be more than onecontrol model that may qualify for being associated with a given clientjob. Accordingly, in some embodiments, control engines may be arrangedto randomly select a control model from among the two or more controlmodels to associate with a given client job. In this context, randomselection of control models may be based on rules or instructionsprovided via configuration information. For example, in someembodiments, one or more control models may be weighted to be selectedmore often than others.

In one or more of the various embodiments, one or more control modelsmay be considered experimental in the sense that they may be introducedinto the file system to evaluate their performance as compared againstother control models. For example, in some embodiments, control enginesmay be arranged to associate experimental control models for Job Type Awith 10% of Type A client jobs. Note, in one or more of the variousembodiments, control engines may be arranged to employ configurationinformation to determine selection weights that may be associated withcontrol models to account for local circumstances or local requirements.

At block 1206, in one or more of the various embodiments, the controlengine may be arranged to execute the one or more client jobs based ontarget scores generated by the associated control models. As describedabove, control engines may be arranged to employ control models todetermine how a scheduler may select or allocate commands to one or morestorage devices.

At block 1208, in one or more of the various embodiments, the controlengine may be arranged to grade the one or more control models based onthe performance quality of the one or more associated client jobs. Inone or more of the various embodiments, control engines may be arrangedto monitor one or more efficiency or performance metrics associated withcontrol models.

In some embodiments, the particular metrics may vary depending on thetype of client job. In some embodiments, such metrics may be related totracking whether client jobs may be meeting various criteria. Forexample, in some embodiments, such criteria may include, meeting orexceeding completion deadlines or timeouts, utilization goals, latencyor responsiveness goals, or the like.

Also, in one or more of the various embodiments, direct user feedbackmay be included a grading process as well, such as, feedback provided bysurveys, or the like. Further, in some embodiments, passive monitoringof user activity may be employed to provide one or more metrics that maybe employed in grading control models. For example, for someembodiments, in some cases, user dissatisfaction may be inferred if thesame read operation is requested in rapid succession, perhaps indicatingthat the user is repeatedly executing the same operation (e.g., multiplemouse clicks to open a file) because the file system may seemunresponsive to that user.

Accordingly, in one or more of the various embodiments, control enginesmay be arranged to associate a performance grade or score with eachcontrol model. Note, in some embodiments, even if alternative controlmodels may be unavailable for a given job type, grades may be maintainedfor control models.

At decision block 1210, in one or more of the various embodiments, ifone or more grades associated with one or more control models may bebelow a threshold value, control may flow to block 1212; otherwise,control may flow to decision block 1214. In one or more of the variousembodiments, control engines may be arranged to periodically evaluatecontrol model grades to determine if there may be one or more controlmodels that may be performing below standard.

Also, in one or more of the various embodiments, if experimental controlmodels have been employed, grades of the experimental control models maybe compared with grades of non-experimental control models. Accordingly,in one or more of the various embodiments, experimental control modelsthat may have better performance than their counterpart productioncontrol models may be identified.

At block 1212, in one or more of the various embodiments, the controlengine may be arranged to generate one or more notifications or one ormore reports associated with the one or more control models that mayhave poor grades. In one or more of the various embodiments, controlengines may be arranged to generate one or more notifications or reportsthat include information about control model grades. In someembodiments, these may include information about some or all controlmodel grades. Also, in some embodiments, notifications or reports may bearranged to include information associated with experimental controlmodels, such as, how they may compare to their non-experimentalcounterparts.

In one or more of the various embodiments, control engines may bearranged to determine notification or report formats, content, deliverymechanisms, delivery targets, or the like, based on configurationinformation to account for local circumstances or local requirements.

Also, in one or more of the various embodiments, control engines may bearranged to automatically put one or more experimental control modelsinto regular use based on their grades exceeding the grades of theirnon-experimental counterparts. Further, in one or more of the variousembodiments, control engines may be arranged to modify the selectionweights associated with experimental control models. For example, in oneor more of the various embodiments, if an experimental control modelassociated with a 10% selection weight has graded well, its weight maybe automatically increased to 20%, or the like.

At decision block 1214, in one or more of the various embodiments, ifthere may be more client jobs, control may loop back to block 1202;otherwise, control may be returned to a calling process.

It will be understood that each block in each flowchart illustration,and combinations of blocks in each flowchart illustration, can beimplemented by computer program instructions. These program instructionsmay be provided to a processor to produce a machine, such that theinstructions, which execute on the processor, create means forimplementing the actions specified in each flowchart block or blocks.The computer program instructions may be executed by a processor tocause a series of operational steps to be performed by the processor toproduce a computer-implemented process such that the instructions, whichexecute on the processor, provide steps for implementing the actionsspecified in each flowchart block or blocks. The computer programinstructions may also cause at least some of the operational steps shownin the blocks of each flowchart to be performed in parallel. Moreover,some of the steps may also be performed across more than one processor,such as might arise in a multi-processor computer system. In addition,one or more blocks or combinations of blocks in each flowchartillustration may also be performed concurrently with other blocks orcombinations of blocks, or even in a different sequence than illustratedwithout departing from the scope or spirit of the invention.

Accordingly, each block in each flowchart illustration supportscombinations of means for performing the specified actions, combinationsof steps for performing the specified actions and program instructionmeans for performing the specified actions. It will also be understoodthat each block in each flowchart illustration, and combinations ofblocks in each flowchart illustration, can be implemented by specialpurpose hardware based systems, which perform the specified actions orsteps, or combinations of special purpose hardware and computerinstructions. The foregoing example should not be construed as limitingor exhaustive, but rather, an illustrative use case to show animplementation of at least one of the various embodiments of theinvention.

Further, in one or more embodiments (not shown in the figures), thelogic in the illustrative flowcharts may be executed using an embeddedlogic hardware device instead of a CPU, such as, an Application SpecificIntegrated Circuit (ASIC), Field Programmable Gate Array (FPGA),Programmable Array Logic (PAL), or the like, or combination thereof. Theembedded logic hardware device may directly execute its embedded logicto perform actions. In one or more embodiments, a microcontroller may bearranged to directly execute its own embedded logic to perform actionsand access its own internal memory and its own external Input and OutputInterfaces (e.g., hardware pins or wireless transceivers) to performactions, such as System On a Chip (SOC), or the like.

The invention claimed is:
 1. A method for managing file systems over anetwork using one or more processors that execute instructions toperform actions, comprising: providing one or more jobs for one or morestorage computers in a file system, wherein the one or more storagecomputers are associated with one or more storage devices; generatingone or more error values that correspond to the one or more jobs basedon a setpoint value and a status value that are determined for each joband defined in one or more control models; associating the one or morecontrol models with the one or more jobs, wherein each control modelgenerates a plurality of partial target scores based on the one or moreerror values for each corresponding job that are accumulated to providea target score that corresponds to each control model, and wherein thetarget score is employed to control a speed of completion for the one ormore jobs associated with each control model; selecting one or morecommands for execution on the one or more storage devices based on theone or more commands for a job being associated with one target scorethat corresponds to one control model and is greater than one or moreother target scores associated with one or more other control models,and wherein each control model is also graded in comparison to eachother control model based on performance quality of one or more jobsassociated with each graded control model; updating the one or morescores based on the one or more graded control models and one or moremetrics; and ranking the one or more jobs based on the one or moreupdated scores, wherein one or more subsequent commands are selected andexecuted based on the ranking of the one or more jobs.
 2. The method ofclaim 1, further comprising: generating one or more scores based on theone or more control models associated with each job, wherein each job isassociated with a score provided by its associated control model, andwherein each job that is behind its corresponding schedule is associatedwith a higher score value than each other job that is either on itscorresponding other schedule or ahead of its corresponding otherschedule.
 3. The method of claim 1, wherein the one or more partialtarget scores further comprise: a first partial target scoreproportional to a magnitude of an error value; a second partial targetscore based on a historic cumulative error associated with the one ormore jobs; and a third partial target score based on a current rate ofchange of the error value, and wherein the first, second and thirdpartial target scores are accumulated to provide a target score.
 4. Themethod of claim 1, wherein the updating of the one or more scoresfurther comprises: employing one or more metrics to update the one ormore scores.
 5. The method of claim 1, wherein associating the one ormore control models with the one or more jobs, further comprises:determining one or more interactive jobs and one or more long-runningjobs based on the one or more characteristic; associating eachinteractive job with a first type of control model, wherein target scorevalues generated by the first type of control model are equivalent tozero; and associating each long-running job with a second type ofcontrol model, wherein target score values generated by the second typeof control model are based on a remainder of work to be completed by theeach long-running job.
 6. The method of claim 1, wherein updating theone or more scores, further comprises: employing the one or more controlmodels and one or more metrics to determine one or more executing jobsthat are one or more of ahead-of-schedule, on-schedule, orbehind-schedule; increasing each score associated with eachbehind-schedule executing job; decreasing each score associated witheach ahead-of-schedule executing job; and setting each score associatedwith each on-schedule job to zero.
 7. The method of claim 1, whereinselecting the one or more commands for execution, further comprises oneor more of: increasing a number of commands for the one or more jobsthat are behind schedule; or decreasing the number of commands for theone or more other jobs when the one or more jobs that are ahead ofschedule.
 8. A system for managing a file system over a networkcomprising: a network computer, comprising: a memory that stores atleast instructions; and one or more processors that execute instructionsthat perform actions, including: providing one or more jobs for one ormore storage computers in a file system, wherein the one or more storagecomputers are associated with one or more storage devices; generatingone or more error values that correspond to the one or more jobs basedon a setpoint value and a status value that are determined for each joband defined in one or more control models; associating the one or morecontrol models with the one or more jobs, wherein each control modelgenerates a plurality of partial target scores based on the one or moreerror values for each corresponding job that are accumulated to providea target score that corresponds to each control model, and wherein thetarget score is employed to control a speed of completion for the one ormore jobs associated with each control model; selecting one or morecommands for execution on the one or more storage devices based on theone or more commands for a job being associated with one target scorethat corresponds to one control model and is greater than one or moreother target scores associated with one or more other control models,and wherein each control model is also graded in comparison to eachother control model based on performance quality of one or more jobsassociated with each graded control model; updating the one or morescores based on the one or more graded control models and one or moremetrics; and ranking the one or more jobs based on the one or moreupdated scores, wherein one or more subsequent commands are selected andexecuted based on the ranking of the one or more jobs.
 9. The system ofclaim 8, further comprising: generating one or more scores based on theone or more control models associated with each job, wherein each job isassociated with a score provided by its associated control model, andwherein each job that is behind its corresponding schedule is associatedwith a higher score value than each other job that is either on itscorresponding other schedule or ahead of its corresponding otherschedule.
 10. The system of claim 8, wherein the one or more partialtarget scores further comprise: a first partial target scoreproportional to a magnitude of an error value; a second partial targetscore based on a historic cumulative error associated with the one ormore jobs; and a third partial target score based on a current rate ofchange of the error value, and wherein the first, second and thirdpartial target scores are accumulated to provide a target score.
 11. Thesystem of claim 8, wherein the updating of the one or more scoresfurther comprises: employing one or more metrics to update the one ormore scores.
 12. The system of claim 8, wherein associating the one ormore control models with the one or more jobs, further comprises:determining one or more interactive jobs and one or more long-runningjobs based on the one or more characteristic; associating eachinteractive job with a first type of control model, wherein target scorevalues generated by the first type of control model are equivalent tozero; and associating each long-running job with a second type ofcontrol model, wherein target score values generated by the second typeof control model are based on a remainder of work to be completed by theeach long-running job.
 13. The system of claim 8, wherein updating theone or more scores, further comprises: employing the one or more controlmodels and one or more metrics to determine one or more executing jobsthat are one or more of ahead-of-schedule, on-schedule, orbehind-schedule; increasing each score associated with eachbehind-schedule executing job; decreasing each score associated witheach ahead-of-schedule executing job; and setting each score associatedwith each on-schedule job to zero.
 14. The system of claim 8, whereinselecting the one or more commands for execution, further comprises oneor more of: increasing a number of commands for the one or more jobsthat are behind schedule; or decreasing the number of commands for theone or more other jobs when the one or more jobs that are ahead ofschedule.
 15. A processor readable non-transitory storage media thatincludes instructions for managing a file system over a network, whereinexecution of the instructions by one or more processors on one or morenetwork computers performs actions, comprising: providing one or morejobs for one or more storage computers in a file system, wherein the oneor more storage computers are associated with one or more storagedevices; generating one or more error values that correspond to the oneor more jobs based on a setpoint value and a status value that aredetermined for each job and defined in one or more control models;associating the one or more control models with the one or more jobs,wherein each control model generates a plurality of partial targetscores based on the one or more error values for each corresponding jobthat are accumulated to provide a target score that corresponds to eachcontrol model, and wherein the target score is employed to control aspeed of completion for the one or more jobs associated with eachcontrol model; selecting one or more commands for execution on the oneor more storage devices based on the one or more commands for a jobbeing associated with one target score that corresponds to one controlmodel and is greater than one or more other target scores associatedwith one or more other control models, and wherein each control model isalso graded in comparison to each other control model based onperformance quality of one or more jobs associated with each gradedcontrol model; updating the one or more scores based on the one or moregraded control models and one or more metrics; and ranking the one ormore jobs based on the one or more updated scores, wherein one or moresubsequent commands are selected and executed based on the ranking ofthe one or more jobs.
 16. The processor readable non-transitory storagemedia of claim 15, further comprising: generating one or more scoresbased on the one or more control models associated with each job,wherein each job is associated with a score provided by its associatedcontrol model, and wherein each job that is behind its correspondingschedule is associated with a higher score value than each other jobthat is either on its corresponding other schedule or ahead of itscorresponding other schedule.
 17. The processor readable non-transitorystorage media of claim 15, further comprising: a first partial targetscore proportional to a magnitude of an error value; a second partialtarget score based on a historic cumulative error associated with theone or more jobs; and a third partial target score based on a currentrate of change of the error value, and wherein the first, second andthird partial target scores are accumulated to provide a target score.18. The processor readable non-transitory storage media of claim 15,wherein the updating of the one or more scores, further comprises:employing one or more metrics to update the one or more scores.
 19. Theprocessor readable non-transitory storage media of claim 15, whereinassociating the one or more control models with the one or more jobs,further comprises: determining one or more interactive jobs and one ormore long-running jobs based on the one or more characteristic;associating each interactive job with a first type of control model,wherein target score values generated by the first type of control modelare equivalent to zero; and associating each long-running job with asecond type of control model, wherein target score values generated bythe second type of control model are based on a remainder of work to becompleted by the each long-running job.
 20. The processor readablenon-transitory storage media of claim 15, wherein updating the one ormore scores, further comprises: employing the one or more control modelsand one or more metrics to determine one or more executing jobs that areone or more of ahead-of-schedule, on-schedule, or behind-schedule;increasing each score associated with each behind-schedule executingjob; decreasing each score associated with each ahead-of-scheduleexecuting job; and setting each score associated with each on-schedulejob to zero.